7f451fdf-bc04-4b49-a20d-0d667758e828 beie director@blueeyesintelligence.org 10.35940/ijitee.C8364.0110321 10.1109/ICICCS.2016.7542360Gupta, S., & Tripathi, P. (2016, February). An emerging trend of big data analytics with health insurance in India. In 2016 International Conference on Innovation and Challenges in Cyber Security (ICICCS-INBUSH) (pp. 64-69). IEEE.‏Kaggle Medical Cost Personal Datasets. Kaggle Inc. https://www.kaggle.com/mirichoi0218/insurance.RisksPesantez-Narvaez7270201910.3390/risks7020070Predicting motor insurance claims using telematics data-XGBoost versus logistic regressionPesantez-Narvaez, J., Guillen, M., & Alcañiz, M. (2019). Predicting motor insurance claims using telematics data-XGBoost versus logistic regression. Risks, 7(2), 7010.1109/BigMM.2019.00-25Singh, R., Ayyar, M. P., Pavan, T. S., Gosain, S., & Shah, R. R. (2019, September). Automating Car Insurance Claims Using Deep Learning Techniques. In 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM) (pp. 199-207). IEEE.‏Stucki, O. (2019). Predicting the customer churn with machine learning methods: case: private insurance customer data.‏Bmj 338Sterne200910.1136/bmj.b2393Multiple imputation for missing data in epidemiological and clinical research: potential and pitfallsSterne, J. A., White, I. R., Carlin, J. B., Spratt, M., Royston, P., Kenward, M. G., ... & Carpenter, J. R. (2009). Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. Bmj, 338.‏10.1201/9780429492259Van Buuren, S. (2018). Flexible imputation of missing data. CRC press.‏Fauzan, M. A., & Murfi, H. (2018). The accuracy of XGBoost for insurance claim prediction. Int. J. Adv. Soft Comput. Appl, 10(2).‏10.1109/ICICCT.2018.8473034Kowshalya, G., & Nandhini, M. (2018, April). Predicting fraudulent claims in automobile insurance. In 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT) (pp. 1338-1343). IEEE.‏10.1109/EMES.2017.7980368Kayri, M., Kayri, I., & Gencoglu, M. T. (2017, June). The performance comparison of multiple linear regression, random forest and artificial neural network by using photovoltaic and atmospheric data. In 2017 14th International Conference on Engineering of Modern Electric Systems (EMES) (pp. 1-4). IEEE.‏10Denuit201910.1007/978-3-030-25820-7Effective Statistical Learning Methods for Actuaries I: GLMs and ExtensionsDenuit, Michel & Hainaut, Donatien & Trufin, Julien. (2019). Effective Statistical Learning Methods for Actuaries I: GLMs and Extensions. 10.1007/978-3-030-25820-7.10.1023/A:1010933404324Breiman, Leo. 2001. "Random Forests." Machine Learning 45 (1). Springer: 5-32.10.1145/2939672.2939785Chen, T., & Guestrin, C. (2016). XGBoost: a scalable tree boosting system 22nd ACM SIGKDD Int. In Conf. on Knowledge Discovery and Data Mining.‏In Solar Energy vol 150Aler558201710.1016/j.solener.2017.05.018Improving the separation of direct and diffuse solar radiation components using machine learning by gradient boostingAler, R., Galván, I.M., Ruiz-Arias, J.A., Gueymard, C.A. (2017). Improving the separation of direct and diffuse solar radiation components using machine learning by gradient boosting. In Solar Energy vol. 150, pp. 558-569.In Proceedings of the Recommender Systems Challenge 2017 (ppVolkovs201710.1145/3124791.3124792Content-based neighbor models for cold start in recommender systemsVolkovs, M., Yu, G. W., & Poutanen, T. (2017). Content-based neighbor models for cold start in recommender systems. In Proceedings of the Recommender Systems Challenge 2017 (pp. 1-6).‏Cunningham, Padraig, and Sarah Jane Delany. 2007. "K-Nearest Neighbour Classifiers." Multiple Classifier Systems 34 (8). Springer New York, NY, USA: 1-1710.1016/j.eswa.2011.08.040Jiang, Shengyi, Guansong Pang, Meiling Wu, and Limin Kuang. 2012. "An Improved K-Nearest-Neighbor Algorithm for Text Categorization." Expert Systems with Applications 39 (1). Elsevier: 1503 9.10.1007/978-3-642-23496-5_13Mccord, Michael, and M Chuah. 2011. "Spam Detection on Twitter Using Traditional Classifiers." In International Conference on Autonomic and Trusted Computing, 175-86. Springer.Machine learningBreiman242123199610.1007/BF00058655Bagging predictorsBreiman, L. (1996). Bagging predictors. Machine learning, 24(2), 123-14010.1214/ss/1009213726Breiman, Leo, and others. 2001. "Statistical Modeling: The Two Cultures (with Comments and a Rejoinder by the Author)." Statistical Science 16 (3). Institute of Mathematical Statistics: 199-231.10.1016/S0167-9473(01)00065-2Friedman. 2002. "Stochastic Gradient Boosting." Computational Statistics & Data Analysis 38 (4). Elsevier: 367-78.10.14569/IJACSA.2018.090238Sabbeh, S. F. (2018). Machine-learning techniques for customer retention: A comparative study. International Journal of Advanced Computer Science and Applications, 9(2).‏Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018). Foundations of machine learning. MIT press.‏Song, Y. Y., & Ying, L. U. (2015). Decision tree methods: applications for classification and prediction. Shanghai archives of psychiatry, 27(2), 130.‏Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1, No. 2). Cambridge: MIT press.‏Kansara, Dhvani & Singh, Rashika & Sanghvi, Deep & Kanani, Pratik. (2018). Improving Accuracy of Real Estate Valuation Using Stacked Regression. Int. J. Eng. Dev. Res. (IJEDR) 6(3), 571-577 (2018)10.5121/ijdkp.2017.7404Yerpude, P., Gudur, V.: Predictive modelling of crime dataset using data mining. Int. J. Data Min. Knowl. Manag. Process (IJDKP) 7(4) (2017)10.1007/978-3-642-21004-4Grosan, C., Abraham, A.: Intelligent Systems: A Modern Approach, Intelligent Systems Reference Library Series. Springer, Cham (2011)